Overview

Dataset statistics

Number of variables14
Number of observations1444
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.1 KiB
Average record size in memory112.1 B

Variable types

Numeric13
Categorical1

Alerts

X.name has a high cardinality: 1444 distinct values High cardinality
z is highly correlated with Lognu and 3 other fieldsHigh correlation
LogFlux1.100m is highly correlated with LogEnergy_Flux100 and 3 other fieldsHigh correlation
LogEnergy_Flux100 is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
LogSignificance is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
Lognu is highly correlated with z and 3 other fieldsHigh correlation
Lognufnu is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
Gaia_G_Magnitude is highly correlated with LognufnuHigh correlation
PL_Index is highly correlated with z and 3 other fieldsHigh correlation
LogVariability_Index is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
LogPivot_Energy is highly correlated with z and 4 other fieldsHigh correlation
LP_Index is highly correlated with z and 3 other fieldsHigh correlation
z is highly correlated with Lognu and 2 other fieldsHigh correlation
LogFlux1.100m is highly correlated with LogEnergy_Flux100 and 3 other fieldsHigh correlation
LogEnergy_Flux100 is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
LogSignificance is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
Lognu is highly correlated with z and 3 other fieldsHigh correlation
Lognufnu is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
Gaia_G_Magnitude is highly correlated with LognufnuHigh correlation
PL_Index is highly correlated with z and 3 other fieldsHigh correlation
LogVariability_Index is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
LogPivot_Energy is highly correlated with z and 4 other fieldsHigh correlation
LP_Index is highly correlated with Lognu and 2 other fieldsHigh correlation
LogFlux1.100m is highly correlated with LogEnergy_Flux100 and 2 other fieldsHigh correlation
LogEnergy_Flux100 is highly correlated with LogFlux1.100m and 2 other fieldsHigh correlation
LogSignificance is highly correlated with LogFlux1.100m and 2 other fieldsHigh correlation
Lognu is highly correlated with PL_Index and 2 other fieldsHigh correlation
PL_Index is highly correlated with Lognu and 2 other fieldsHigh correlation
LogVariability_Index is highly correlated with LogFlux1.100m and 2 other fieldsHigh correlation
LogPivot_Energy is highly correlated with Lognu and 2 other fieldsHigh correlation
LP_Index is highly correlated with Lognu and 2 other fieldsHigh correlation
z is highly correlated with Lognu and 3 other fieldsHigh correlation
LogFlux1.100m is highly correlated with LogEnergy_Flux100 and 6 other fieldsHigh correlation
LogEnergy_Flux100 is highly correlated with LogFlux1.100m and 5 other fieldsHigh correlation
LogSignificance is highly correlated with LogFlux1.100m and 4 other fieldsHigh correlation
Lognu is highly correlated with z and 3 other fieldsHigh correlation
Lognufnu is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
Gaia_G_Magnitude is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
PL_Index is highly correlated with z and 4 other fieldsHigh correlation
LogVariability_Index is highly correlated with LogFlux1.100m and 3 other fieldsHigh correlation
LogPivot_Energy is highly correlated with z and 6 other fieldsHigh correlation
LP_Index is highly correlated with z and 4 other fieldsHigh correlation
LP_beta is highly correlated with LP_IndexHigh correlation
Unnamed: 0 is uniformly distributed Uniform
X.name is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
X.name has unique values Unique
LogSignificance has unique values Unique
LogVariability_Index has unique values Unique
LogPivot_Energy has unique values Unique
LP_Index has unique values Unique
LP_beta has unique values Unique

Reproduction

Analysis started2021-10-14 10:27:06.613792
Analysis finished2021-10-14 10:27:39.287606
Duration32.67 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1444
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean721.5
Minimum0
Maximum1443
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:39.377852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.15
Q1360.75
median721.5
Q31082.25
95-th percentile1370.85
Maximum1443
Range1443
Interquartile range (IQR)721.5

Descriptive statistics

Standard deviation416.9912069
Coefficient of variation (CV)0.5779503908
Kurtosis-1.2
Mean721.5
Median Absolute Deviation (MAD)361
Skewness0
Sum1041846
Variance173881.6667
MonotonicityStrictly increasing
2021-10-14T19:27:39.559559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
9701
 
0.1%
9681
 
0.1%
9671
 
0.1%
9661
 
0.1%
9651
 
0.1%
9641
 
0.1%
9631
 
0.1%
9621
 
0.1%
9611
 
0.1%
Other values (1434)1434
99.3%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
14431
0.1%
14421
0.1%
14411
0.1%
14401
0.1%
14391
0.1%
14381
0.1%
14371
0.1%
14361
0.1%
14351
0.1%
14341
0.1%

X.name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1444
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
4FGL J0001.5+2113
 
1
4FGL J1500.7+4752
 
1
4FGL J1458.6+3722
 
1
4FGL J1457.4-3539
 
1
4FGL J1456.0+5051
 
1
Other values (1439)
1439 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1444 ?
Unique (%)100.0%

Sample

1st row4FGL J0001.5+2113
2nd row4FGL J0003.9-1149
3rd row4FGL J0004.3+4614
4th row4FGL J0004.4-4737
5th row4FGL J0005.9+3824

Common Values

ValueCountFrequency (%)
4FGL J0001.5+21131
 
0.1%
4FGL J1500.7+47521
 
0.1%
4FGL J1458.6+37221
 
0.1%
4FGL J1457.4-35391
 
0.1%
4FGL J1456.0+50511
 
0.1%
4FGL J1454.4-37441
 
0.1%
4FGL J1454.1+16221
 
0.1%
4FGL J1453.5+35051
 
0.1%
4FGL J1451.4+63551
 
0.1%
4FGL J1450.8+52011
 
0.1%
Other values (1434)1434
99.3%

Length

2021-10-14T19:27:39.740903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4fgl1444
50.0%
j0017.5-05141
 
< 0.1%
j0004.4-47371
 
< 0.1%
j0005.9+38241
 
< 0.1%
j0006.3-06201
 
< 0.1%
j0008.0+47111
 
< 0.1%
j0008.4-23391
 
< 0.1%
j0009.1+06281
 
< 0.1%
j0009.8-43171
 
< 0.1%
j0010.6+20431
 
< 0.1%
Other values (1435)1435
49.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

z
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1091
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8243408186
Minimum3.7 × 10-5
Maximum4.313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:39.917966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.7 × 10-5
5-th percentile0.09615
Q10.298908
median0.6285715
Q31.215
95-th percentile2.13895
Maximum4.313
Range4.312963
Interquartile range (IQR)0.916092

Descriptive statistics

Standard deviation0.666680037
Coefficient of variation (CV)0.8087432067
Kurtosis1.353136344
Mean0.8243408186
Median Absolute Deviation (MAD)0.3995695
Skewness1.188324677
Sum1190.348142
Variance0.4444622717
MonotonicityNot monotonic
2021-10-14T19:27:40.118362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78
 
0.6%
0.27
 
0.5%
0.477
 
0.5%
0.66
 
0.4%
0.456
 
0.4%
0.355
 
0.3%
0.2125
 
0.3%
0.255
 
0.3%
0.45
 
0.3%
0.725
 
0.3%
Other values (1081)1385
95.9%
ValueCountFrequency (%)
3.7 × 10-51
0.1%
0.0009271
0.1%
0.0013241
0.1%
0.0023071
0.1%
0.011
0.1%
0.013751
0.1%
0.0181
0.1%
0.0271
0.1%
0.02721
0.1%
0.031
0.1%
ValueCountFrequency (%)
4.3131
0.1%
3.7161
0.1%
3.6477631
0.1%
3.451
0.1%
3.4421
0.1%
3.1641
0.1%
3.1041
0.1%
3.0331
0.1%
3.0131
0.1%
3.011
0.1%

LogFlux1.100m
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct812
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.331524806
Minimum-10.4225082
Maximum-7.060980224
Zeros0
Zeros (%)0.0%
Negative1444
Negative (%)100.0%
Memory size11.4 KiB
2021-10-14T19:27:40.333297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-10.4225082
5-th percentile-9.999351794
Q1-9.721819348
median-9.43062609
Q3-9.035269844
95-th percentile-8.330822764
Maximum-7.060980224
Range3.361527977
Interquartile range (IQR)0.6865495041

Descriptive statistics

Standard deviation0.5198163294
Coefficient of variation (CV)-0.0557054008
Kurtosis0.8056954974
Mean-9.331524806
Median Absolute Deviation (MAD)0.3277016983
Skewness0.9270210759
Sum-13474.72182
Variance0.2702090163
MonotonicityNot monotonic
2021-10-14T19:27:40.703709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.76195389710
 
0.7%
-9.8239087419
 
0.6%
-9.636388028
 
0.6%
-9.7695510797
 
0.5%
-9.9586073157
 
0.5%
-9.6989700047
 
0.5%
-9.6289321386
 
0.4%
-8.9913998286
 
0.4%
-9.7798919126
 
0.4%
-9.6903698336
 
0.4%
Other values (802)1372
95.0%
ValueCountFrequency (%)
-10.42250821
0.1%
-10.341035161
0.1%
-10.316052871
0.1%
-10.277366081
0.1%
-10.259637311
0.1%
-10.245651661
0.1%
-10.229147991
0.1%
-10.226213561
0.1%
-10.215382711
0.1%
-10.209011521
0.1%
ValueCountFrequency (%)
-7.0609802241
0.1%
-7.3142582611
0.1%
-7.376750711
0.1%
-7.4045037781
0.1%
-7.4473317841
0.1%
-7.4596705251
0.1%
-7.6252516541
0.1%
-7.636388021
0.1%
-7.6420651531
0.1%
-7.6882461391
0.1%

LogEnergy_Flux100
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct815
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.20305122
Minimum-12.1414628
Maximum-8.954677021
Zeros0
Zeros (%)0.0%
Negative1444
Negative (%)100.0%
Memory size11.4 KiB
2021-10-14T19:27:40.906101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-12.1414628
5-th percentile-11.82102305
Q1-11.54821356
median-11.29413629
Q3-10.92811799
95-th percentile-10.29722667
Maximum-8.954677021
Range3.186785781
Interquartile range (IQR)0.6200955718

Descriptive statistics

Standard deviation0.4770889527
Coefficient of variation (CV)-0.04258562632
Kurtosis0.9390504788
Mean-11.20305122
Median Absolute Deviation (MAD)0.295930589
Skewness0.9434295772
Sum-16177.20596
Variance0.2276138688
MonotonicityNot monotonic
2021-10-14T19:27:41.096972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11.62160217
 
0.5%
-10.879426077
 
0.5%
-10.954677026
 
0.4%
-11.628932146
 
0.4%
-10.958607316
 
0.4%
-11.8386326
 
0.4%
-11.55595526
 
0.4%
-11.703334816
 
0.4%
-10.96257356
 
0.4%
-10.966576245
 
0.3%
Other values (805)1383
95.8%
ValueCountFrequency (%)
-12.14146281
0.1%
-12.094743951
0.1%
-12.085656841
0.1%
-12.043351421
0.1%
-12.038578911
0.1%
-12.024108861
0.1%
-12.019542111
0.1%
-12.015022871
0.1%
-12.011441041
0.1%
-11.995678631
0.1%
ValueCountFrequency (%)
-8.9546770211
0.1%
-9.3381873141
0.1%
-9.3467874861
0.1%
-9.4509967381
0.1%
-9.5543957971
0.1%
-9.6345120151
0.1%
-9.6575773191
0.1%
-9.6615435061
0.1%
-9.6695862272
0.1%
-9.6757175451
0.1%

LogSignificance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1444
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.199581
Minimum0.4416176625
Maximum2.667596667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:41.304942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4416176625
5-th percentile0.678634662
Q10.9020841615
median1.141570722
Q31.424434609
95-th percentile1.932040889
Maximum2.667596667
Range2.225979004
Interquartile range (IQR)0.522350447

Descriptive statistics

Standard deviation0.3886629581
Coefficient of variation (CV)0.3239989281
Kurtosis0.2253439932
Mean1.199581
Median Absolute Deviation (MAD)0.2615467081
Skewness0.7605589703
Sum1732.194964
Variance0.151058895
MonotonicityNot monotonic
2021-10-14T19:27:41.499972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6447801491
 
0.1%
1.0085730911
 
0.1%
1.1632090371
 
0.1%
1.811859551
 
0.1%
0.65441827361
 
0.1%
1.0397326441
 
0.1%
1.1919416241
 
0.1%
0.97096546791
 
0.1%
1.1888945121
 
0.1%
1.3332380571
 
0.1%
Other values (1434)1434
99.3%
ValueCountFrequency (%)
0.44161766251
0.1%
0.55420637071
0.1%
0.56264004231
0.1%
0.5645951751
0.1%
0.56788835561
0.1%
0.58263945541
0.1%
0.58338704461
0.1%
0.58422097421
0.1%
0.59923989481
0.1%
0.60696125541
0.1%
ValueCountFrequency (%)
2.6675966671
0.1%
2.5439906341
0.1%
2.5125811991
0.1%
2.4744564151
0.1%
2.4674388241
0.1%
2.4648359211
0.1%
2.397782471
0.1%
2.3778650641
0.1%
2.3622535051
0.1%
2.3622209521
0.1%

Lognu
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct540
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.85839754
Minimum11.53529412
Maximum20.25527251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:41.700677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11.53529412
5-th percentile12.29003461
Q112.72509452
median13.30200497
Q314.74973632
95-th percentile16.64048144
Maximum20.25527251
Range8.719978385
Interquartile range (IQR)2.024641794

Descriptive statistics

Standard deviation1.443226404
Coefficient of variation (CV)0.1041409297
Kurtosis0.2945930875
Mean13.85839754
Median Absolute Deviation (MAD)0.7753591375
Skewness0.981032641
Sum20011.52605
Variance2.082902452
MonotonicityNot monotonic
2021-10-14T19:27:41.896937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.0413926927
 
1.9%
12.7250945221
 
1.5%
12.7951845914
 
1.0%
12.7596678413
 
0.9%
12.8401060913
 
0.9%
13.0043213712
 
0.8%
12.6201360512
 
0.8%
12.4099331211
 
0.8%
12.9698816410
 
0.7%
12.5599066310
 
0.7%
Other values (530)1301
90.1%
ValueCountFrequency (%)
11.535294121
 
0.1%
11.570542941
 
0.1%
11.604226051
 
0.1%
11.630427881
 
0.1%
11.780317311
 
0.1%
11.795880021
 
0.1%
11.893761761
 
0.1%
11.89982051
 
0.1%
11.920123332
 
0.1%
11.989894565
0.3%
ValueCountFrequency (%)
20.255272512
0.1%
19.049218021
0.1%
18.680335512
0.1%
18.499687081
0.1%
18.260071391
0.1%
18.008600171
0.1%
17.955206541
0.1%
17.840106091
0.1%
17.790285161
0.1%
17.780317312
0.1%

Lognufnu
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct817
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.78740039
Minimum-13.15490196
Maximum-9.657577319
Zeros0
Zeros (%)0.0%
Negative1444
Negative (%)100.0%
Memory size11.4 KiB
2021-10-14T19:27:42.094196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-13.15490196
5-th percentile-12.5094121
Q1-12.12930384
median-11.84163751
Q3-11.47984448
95-th percentile-10.9152621
Maximum-9.657577319
Range3.497324641
Interquartile range (IQR)0.6494593539

Descriptive statistics

Standard deviation0.489897272
Coefficient of variation (CV)-0.04156109539
Kurtosis0.2812564108
Mean-11.78740039
Median Absolute Deviation (MAD)0.321405755
Skewness0.4886572211
Sum-17021.00616
Variance0.2399993371
MonotonicityNot monotonic
2021-10-14T19:27:42.320999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11.9208187510
 
0.7%
-11.943095158
 
0.6%
-11.974694138
 
0.6%
-11.87289528
 
0.6%
-11.903089998
 
0.6%
-11.767003897
 
0.5%
-11.899629457
 
0.5%
-11.779891917
 
0.5%
-11.931814147
 
0.5%
-11.860120917
 
0.5%
Other values (807)1367
94.7%
ValueCountFrequency (%)
-13.154901961
0.1%
-132
0.1%
-12.987162781
0.1%
-12.832682672
0.1%
-12.829738281
0.1%
-12.818156411
0.1%
-12.798602881
0.1%
-12.777283531
0.1%
-12.752026731
0.1%
-12.749581
0.1%
ValueCountFrequency (%)
-9.6575773191
0.1%
-9.8477116561
0.1%
-10.044312251
0.1%
-10.114073661
0.1%
-10.28232951
0.1%
-10.295849481
0.1%
-10.314258261
0.1%
-10.334419011
0.1%
-10.352617031
0.1%
-10.391473971
0.1%

Gaia_G_Magnitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1225
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.46504536
Minimum12.8144
Maximum21.453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:42.523639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12.8144
5-th percentile16.3604835
Q117.71435
median18.48485
Q319.3734615
95-th percentile20.378855
Maximum21.453
Range8.6386
Interquartile range (IQR)1.6591115

Descriptive statistics

Standard deviation1.250469775
Coefficient of variation (CV)0.06772091544
Kurtosis0.922513269
Mean18.46504536
Median Absolute Deviation (MAD)0.8291
Skewness-0.6020843116
Sum26663.5255
Variance1.563674659
MonotonicityNot monotonic
2021-10-14T19:27:42.713117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.60425
 
0.3%
20.0555
 
0.3%
19.0335
 
0.3%
20.68775
 
0.3%
19.56764
 
0.3%
19.89834
 
0.3%
20.26364
 
0.3%
18.97694
 
0.3%
19.53894
 
0.3%
20.5125624
 
0.3%
Other values (1215)1400
97.0%
ValueCountFrequency (%)
12.81441
0.1%
12.85131
0.1%
13.26951
0.1%
13.84391
0.1%
13.90531
0.1%
14.03411
0.1%
14.07771
0.1%
14.15661
0.1%
14.20371
0.1%
14.55741
0.1%
ValueCountFrequency (%)
21.4531
0.1%
21.2566051
0.1%
21.1965851
0.1%
21.05441
0.1%
20.94691
0.1%
20.8470251
0.1%
20.83931
0.1%
20.79512
0.1%
20.7821
0.1%
20.773831
0.1%

PL_Index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1443
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.246799574
Minimum1.4451753
Maximum3.2412739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:42.901066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.4451753
5-th percentile1.76160711
Q11.99405905
median2.2661127
Q32.46956675
95-th percentile2.757159055
Maximum3.2412739
Range1.7960986
Interquartile range (IQR)0.4755077

Descriptive statistics

Standard deviation0.3145347219
Coefficient of variation (CV)0.1399923365
Kurtosis-0.6499120175
Mean2.246799574
Median Absolute Deviation (MAD)0.2377438
Skewness0.04248993341
Sum3244.378585
Variance0.09893209131
MonotonicityNot monotonic
2021-10-14T19:27:43.103295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.15990352
 
0.1%
2.6593081
 
0.1%
2.10398721
 
0.1%
2.32435131
 
0.1%
2.06031941
 
0.1%
2.55353521
 
0.1%
2.40867921
 
0.1%
2.3631231
 
0.1%
1.82248221
 
0.1%
2.16664771
 
0.1%
Other values (1433)1433
99.2%
ValueCountFrequency (%)
1.44517531
0.1%
1.45392631
0.1%
1.47905791
0.1%
1.4926661
0.1%
1.53307651
0.1%
1.53547451
0.1%
1.55265361
0.1%
1.55945581
0.1%
1.57793371
0.1%
1.58555651
0.1%
ValueCountFrequency (%)
3.24127391
0.1%
3.0609051
0.1%
3.03618221
0.1%
3.02699781
0.1%
3.02271651
0.1%
3.01395251
0.1%
3.01350211
0.1%
3.00755741
0.1%
2.98488861
0.1%
2.9808531
0.1%

LogVariability_Index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1444
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.639959783
Minimum0.3219552462
Maximum4.875129616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:43.304626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3219552462
5-th percentile0.7841565539
Q11.108781849
median1.424859924
Q32.076493324
95-th percentile3.135350755
Maximum4.875129616
Range4.55317437
Interquartile range (IQR)0.9677114753

Descriptive statistics

Standard deviation0.7352501209
Coefficient of variation (CV)0.4483342387
Kurtosis0.7822542711
Mean1.639959783
Median Absolute Deviation (MAD)0.4140613743
Skewness1.04102386
Sum2368.101927
Variance0.5405927403
MonotonicityNot monotonic
2021-10-14T19:27:43.663029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1943526931
 
0.1%
1.0036501071
 
0.1%
1.5758112911
 
0.1%
3.4553907371
 
0.1%
0.87119521531
 
0.1%
1.7432804851
 
0.1%
1.556007371
 
0.1%
1.7020095921
 
0.1%
1.4701711661
 
0.1%
1.0662258211
 
0.1%
Other values (1434)1434
99.3%
ValueCountFrequency (%)
0.32195524621
0.1%
0.32939893711
0.1%
0.33394563111
0.1%
0.38615540711
0.1%
0.40292746611
0.1%
0.41303652531
0.1%
0.41760453541
0.1%
0.42884108231
0.1%
0.45530629271
0.1%
0.49329786931
0.1%
ValueCountFrequency (%)
4.8751296161
0.1%
4.7510123391
0.1%
4.4788737841
0.1%
4.3094575441
0.1%
4.1319912471
0.1%
4.1307307891
0.1%
4.1199155091
0.1%
3.9900275431
0.1%
3.8870307761
0.1%
3.882919891
0.1%

LogPivot_Energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1444
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.152319179
Minimum2.366145705
Maximum4.299102102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:43.860732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.366145705
5-th percentile2.650244924
Q12.874285334
median3.096459595
Q33.42285632
95-th percentile3.751451765
Maximum4.299102102
Range1.932956398
Interquartile range (IQR)0.5485709859

Descriptive statistics

Standard deviation0.3565473739
Coefficient of variation (CV)0.1131063682
Kurtosis-0.5275927037
Mean3.152319179
Median Absolute Deviation (MAD)0.2670487429
Skewness0.3849479387
Sum4551.948895
Variance0.1271260298
MonotonicityNot monotonic
2021-10-14T19:27:44.061873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5511869261
 
0.1%
3.166323171
 
0.1%
3.2140618381
 
0.1%
2.8663903171
 
0.1%
3.6906931071
 
0.1%
2.9996761131
 
0.1%
2.9650340961
 
0.1%
3.0550087181
 
0.1%
3.6000746431
 
0.1%
3.2807708471
 
0.1%
Other values (1434)1434
99.3%
ValueCountFrequency (%)
2.3661457051
0.1%
2.3772792531
0.1%
2.3872785331
0.1%
2.4371681231
0.1%
2.4526292991
0.1%
2.4602964321
0.1%
2.4680144651
0.1%
2.4864580231
0.1%
2.4873120161
0.1%
2.4888298581
0.1%
ValueCountFrequency (%)
4.2991021021
0.1%
4.272890821
0.1%
4.1984945531
0.1%
4.1685037891
0.1%
4.167534931
0.1%
4.1524979331
0.1%
4.1343259921
0.1%
4.1092111821
0.1%
4.1041299011
0.1%
4.0891497361
0.1%

LP_Index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1444
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.17247949
Minimum1.068352
Maximum3.3003483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2021-10-14T19:27:44.289361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.068352
5-th percentile1.61883316
Q11.92090745
median2.19448435
Q32.4125414
95-th percentile2.696674855
Maximum3.3003483
Range2.2319963
Interquartile range (IQR)0.49163395

Descriptive statistics

Standard deviation0.3410594184
Coefficient of variation (CV)0.1569908577
Kurtosis-0.2461947324
Mean2.17247949
Median Absolute Deviation (MAD)0.2411121
Skewness-0.1218854972
Sum3137.060384
Variance0.1163215269
MonotonicityNot monotonic
2021-10-14T19:27:44.484941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.54815051
 
0.1%
2.31715371
 
0.1%
2.13996841
 
0.1%
2.17527461
 
0.1%
2.09479861
 
0.1%
2.4775231
 
0.1%
2.2911911
 
0.1%
2.293051
 
0.1%
1.83162071
 
0.1%
2.0785981
 
0.1%
Other values (1434)1434
99.3%
ValueCountFrequency (%)
1.0683521
0.1%
1.13931941
0.1%
1.16095481
0.1%
1.18944031
0.1%
1.20865021
0.1%
1.22190821
0.1%
1.26321921
0.1%
1.28460791
0.1%
1.31402751
0.1%
1.33556381
0.1%
ValueCountFrequency (%)
3.30034831
0.1%
3.08826781
0.1%
3.079451
0.1%
3.07250071
0.1%
3.04047631
0.1%
3.02233081
0.1%
3.01530531
0.1%
3.00763231
0.1%
2.99605871
0.1%
2.99496751
0.1%

LP_beta
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1444
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09666158504
Minimum-0.12630665
Maximum0.6955592
Zeros0
Zeros (%)0.0%
Negative166
Negative (%)11.5%
Memory size11.4 KiB
2021-10-14T19:27:44.714414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.12630665
5-th percentile-0.03687580405
Q10.034450276
median0.0805024075
Q30.1348395225
95-th percentile0.288959118
Maximum0.6955592
Range0.82186585
Interquartile range (IQR)0.1003892465

Descriptive statistics

Standard deviation0.1052134884
Coefficient of variation (CV)1.088472617
Kurtosis5.108512181
Mean0.09666158504
Median Absolute Deviation (MAD)0.049579775
Skewness1.651761605
Sum139.5793288
Variance0.01106987814
MonotonicityNot monotonic
2021-10-14T19:27:44.899520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.158776071
 
0.1%
0.136813221
 
0.1%
0.054309341
 
0.1%
0.170263661
 
0.1%
-0.0462706651
 
0.1%
0.200561451
 
0.1%
0.167007551
 
0.1%
0.073060421
 
0.1%
-0.0081831971
 
0.1%
0.111524321
 
0.1%
Other values (1434)1434
99.3%
ValueCountFrequency (%)
-0.126306651
0.1%
-0.12434421
0.1%
-0.123350341
0.1%
-0.120314851
0.1%
-0.114960141
0.1%
-0.1145746041
0.1%
-0.111616471
0.1%
-0.107556731
0.1%
-0.103075371
0.1%
-0.0975145851
0.1%
ValueCountFrequency (%)
0.69555921
0.1%
0.68338691
0.1%
0.67972161
0.1%
0.646969261
0.1%
0.62760071
0.1%
0.597055441
0.1%
0.568982361
0.1%
0.550132161
0.1%
0.544146241
0.1%
0.53583641
0.1%

Interactions

2021-10-14T19:27:36.386639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:09.645835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:11.963927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:14.183693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:16.551079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:18.700565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:20.932780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:22.986559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:25.383561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:27.460787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:29.888133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:31.935342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:34.327745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:36.550814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:09.893738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:12.147551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:14.355967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:16.721011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:18.862537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:21.091815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:23.161356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:25.540757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:27.630740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:30.048122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:32.115351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:34.487164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:36.703861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:10.045664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:12.470913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:14.513954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:16.875559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:19.009101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:21.235136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:23.317038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:25.683290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:27.787076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:30.208839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:32.276488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:34.631948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:37.061677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:10.249651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:12.634203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:14.691761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:17.049556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:19.174050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:21.403434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:23.492241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:25.847220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:27.964035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:30.374781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:32.451773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:34.798400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:37.220255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:10.438636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:12.792311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:14.864298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:17.218765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:19.341641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:21.572179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:23.662758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:26.047306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:28.137867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:30.535390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:32.636281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:34.962173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:37.365675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:10.606942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:12.939290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:15.025182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:17.376844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:19.502949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:21.722650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:23.822291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:26.204930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:28.356360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:30.685275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:32.970618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:35.135976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:37.508166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:10.765206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:13.082798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:15.184122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:17.536495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:19.649766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:21.867083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:23.985993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:26.364358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:28.512056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:30.832060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:33.127450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:35.297816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:37.668349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:10.942771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:13.238827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:15.360491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:17.712371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:19.822837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:22.037355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:24.181104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:26.525637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:28.875736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:30.995393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:33.302595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:35.461664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:37.807368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:11.098131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:13.407669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:15.516088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:17.877701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:19.981059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:22.188205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:24.362170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:26.681592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:29.044023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:31.139533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:33.459972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:35.603817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:37.971228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:11.276826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:13.578792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:15.702775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:18.051664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:20.144516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:22.368970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:24.727726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:26.846244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:29.235938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:31.313826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:33.656688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:35.767319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:38.126912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:11.438109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:13.732996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:15.867478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:18.210454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:20.293617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:22.526400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:24.886760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:27.014644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:29.397909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:31.479138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:33.832646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:35.922112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:38.288355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:11.622597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:13.896851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:16.053504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:18.385339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:20.640466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:22.693318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:25.061770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:27.174907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:29.575407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:31.643676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:34.006611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:36.085561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:38.446547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:11.792505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:14.043892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:16.215653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:18.543236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:20.788884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:22.844624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:25.221277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:27.318928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:29.734925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:31.792276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:34.165257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T19:27:36.235294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-14T19:27:45.123388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-14T19:27:45.403718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-14T19:27:45.674563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-14T19:27:45.947951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-14T19:27:38.748428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-14T19:27:39.129971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0X.namezLogFlux1.100mLogEnergy_Flux100LogSignificanceLognuLognufnuGaia_G_MagnitudePL_IndexLogVariability_IndexLogPivot_EnergyLP_IndexLP_beta
004FGL J0001.5+21131.106000-8.866461-10.7144431.64478014.198657-11.93930218.4836002.6593083.1943532.5511872.5481510.158776
114FGL J0003.9-11491.309990-9.454693-11.4934951.09224612.389166-12.08777819.3223002.0871090.6393443.2246461.9320690.271187
224FGL J0004.3+46141.810000-9.617983-11.6038010.85217812.409933-12.64016520.4082972.5849691.6518622.9624272.5269710.067292
334FGL J0004.4-47370.880000-9.360514-11.1864191.29120813.120574-11.59859919.8476002.3655602.1433992.9616982.3042860.092037
444FGL J0005.9+38240.229000-9.371611-11.0777941.21631813.120574-11.13906318.3994002.6721161.4276412.6532732.5520500.189904
554FGL J0006.3-06200.347000-9.853872-11.8446640.72056212.920123-11.08724717.1541002.1278441.3098983.3348982.1056310.169535
664FGL J0008.0+47110.280000-8.688246-10.7375491.67386413.525045-11.88605719.0094002.0470490.8084813.1343561.9286300.079131
774FGL J0008.4-23390.147000-9.785156-11.5702481.01483416.910091-11.93181418.3753001.6560621.1409653.8170381.3491110.278169
884FGL J0009.1+06281.562629-9.321482-11.2898831.18023113.389166-11.76700417.9922002.0990551.1154773.2745072.0630700.046000
994FGL J0009.8-43170.560000-9.684030-11.6143941.02592214.103804-12.01278118.9408002.0651711.2284613.3271612.0467580.022229

Last rows

Unnamed: 0X.namezLogFlux1.100mLogEnergy_Flux100LogSignificanceLognuLognufnuGaia_G_MagnitudePL_IndexLogVariability_IndexLogPivot_EnergyLP_IndexLP_beta
143414344FGL J1717.6-51541.158-9.027797-10.9546771.17594912.245513-12.13548919.0106262.5869352.4564932.9969432.5181050.386508
143514354FGL J1802.6-39400.296-8.315155-10.2510371.81308412.698970-11.31966418.5072272.3674353.5011502.9278212.2640280.129025
143614364FGL J1830.1+06170.745-9.070070-10.8961961.12379512.589950-11.72584219.3797572.3734542.4015453.1967032.3196480.092991
143714374FGL J1833.6-21032.507-7.844664-9.6757182.10307112.164353-11.44009320.6710322.5344593.3933882.7926492.4562810.095306
143814384FGL J1955.2+13580.743-9.031984-10.9706161.22539912.800029-11.20204019.3641012.4087202.2496553.0141122.2768240.182890
143914394FGL J2015.5+37100.859-8.094204-10.1079051.39061212.545307-10.96657620.3937072.4528532.0985493.4906512.7052150.177748
144014404FGL J2038.7+51171.686-8.966576-10.9665761.11483212.469822-11.53165320.0361882.5731891.9034893.0022002.3532670.307681
144114414FGL J2039.5+52180.053-9.331614-11.2456521.11232616.759668-11.63827219.4959181.8391290.8178333.8256121.7976850.047917
144214424FGL J2201.8+50481.899-8.519993-10.2225731.75779812.451786-11.94309518.7155532.6604493.4943882.6833232.5626820.142221
144314434FGL J2347.0+51410.044-8.545155-10.5058451.80157716.195900-10.89279016.6152521.8144211.7814503.2866201.7465010.036544